US8099311B2 - System and method for routing tasks to a user in a workforce - Google Patents
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- US8099311B2 US8099311B2 US12/072,382 US7238208A US8099311B2 US 8099311 B2 US8099311 B2 US 8099311B2 US 7238208 A US7238208 A US 7238208A US 8099311 B2 US8099311 B2 US 8099311B2
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
- G06Q10/063112—Skill-based matching of a person or a group to a task
Definitions
- the present invention relates generally to management of a workforce and more specifically to allocating a task to a user in the workforce.
- Assigning tasks to members of a workforce is typically performed by randomly assigning members or by a manager who supervises a defined number of members. These methods become inefficient as the workforce expands or as information about members becomes outdated and/or sparse. Further, some workforces, such as those that exist through social networks on the Internet, may be unable to collect reliable data about an incoming member. Therefore, there is a need for allocating tasks to members of large workforces where a limited amount of information about each member is known.
- a system and method for allocating a task to one of a plurality of users is provided.
- a task includes a set of task attributes which characterize the task.
- the allocation of the task is performed using a neural network based on skill metrics associated with each user of the plurality of users and performance indicators associated with past behaviors of each user of the plurality of users.
- the task is then routed to the user and feedback is collected from whomever submitted the task and feedback is also collected based on the user's behavior.
- a method for allocating a task to a user is provided.
- a task including at least one normalized task attribute is received.
- a plurality of profiles is accessed, and each profile is associated with a user and including a plurality of skill metrics and a plurality of performance indicators of the user.
- An initial value for each profile is calculated based on the at least one normalized task attribute and the plurality of skill metrics.
- a fitness metric for each profile is calculated based on the initial value using a neural network having weights based on the plurality of performance indicators.
- a profile is selected based on a stochastic model using the fitness metric.
- the task is routed to the user associated with the selected profile.
- an input module is configured to receive a task including at least one normalized task attribute.
- a profile module is configured to access a plurality of profiles, and each profile is associated with a user and including a plurality of skill metrics and a plurality of performance indicators of the user.
- a supervised learning module is configured to calculate an initial value for each profile based on the at least one normalized task attribute and the plurality of skill metrics.
- An unsupervised learning module is configured to calculate a fitness metric for each individual profile based on the initial value using a neural network having weights based on the plurality of performance indicators.
- a stochastic module is configured to select a profile based on a stochastic model using the fitness metric.
- An output module is configured to route the task to the user associated with the selected profile.
- a computer readable medium having embodied thereon a program executable by a processor for executing a method for allocating a task.
- a task including at least one normalized task attribute is received.
- a plurality of profiles is accessed, and each profile is associated with a user and including a plurality of skill metrics and a plurality of performance indicators of the user.
- An initial value for each profile is calculated based on the at least one normalized task attribute and the plurality of skill metrics.
- a fitness metric for each profile is calculated based on the initial value using a neural network having weights based on the plurality of performance indicators.
- a profile is selected based on a stochastic model using the fitness metric. The task is routed to the user associated with the selected profile.
- FIG. 1 is a diagram of a computing network including a routing system for allocating a task to a user according to various embodiments.
- FIG. 2 is a block diagram of the routing system for allocating a task to a user according to various embodiments.
- FIG. 3A is an exemplary task attribute tree according to various embodiments.
- FIG. 3B is a table depicting the skill attributes of various users according to various embodiments.
- FIG. 4 is a diagram of an exemplary neural network according to various embodiments.
- FIG. 5A is a depiction of a neural network associated with a-first user according to various embodiments.
- FIG. 5B is a depiction of a neural network associated with a second user according to various embodiments.
- FIG. 5C is a depiction of a neural network associated with a third user according to various embodiments.
- FIG. 5D is a depiction of a neural network associated with a fourth user according to various embodiments.
- FIG. 6 is a depiction of a selection of a user according to various embodiments.
- FIG. 7 is a depiction of an updated neural network associated with the second user according to various embodiments.
- FIG. 8 is a flowchart of a method for allocating a task to a user according to various embodiments.
- a task may include, for example, a problem to be solved, a question to be answered, a survey or questionnaire, a request for proposals, or the like.
- a task including normalized task attributes is received by a routing system.
- the normalized task attributes correspond to skill metrics within a profile of each user.
- a supervised learning portion of a neural network calculates an initial value based on the skill metrics of each user and the normalized task attributes. The initial value associated with each profile is input into an unsupervised learning portion of the neural network having weights.
- the weights are based on measures of behavior of a user, expressed quantitatively by performance indicators within the profile of each user.
- a neural network is generated to calculate a fitness metric.
- a stochastic model is used to select a profile based in part on the fitness metric. The task is then routed to the user associated with the selected profile and feedback is collected from the individual and the selected user.
- Users may include traditional employees, contractors, outsourcing companies, members of social networks, and the like. In these workforces, a limited amount of information may be known about actual skills, demographics, or behaviors of each user. In some embodiments, users also include non-human systems. For example, a task may be allocated to a non-human system such as a website, an external system, an interactive voice responder, or other automated system.
- FIG. 1 is a diagram of a computing network 100 including a routing system for allocating a task to a user according to various embodiments.
- the environment 100 includes an individual 102 , a routing system 104 and a plurality of users 106 .
- the individual 102 , the routing system 104 , and the plurality of users 106 are configured to communicate via a communication network 108 .
- the individual 102 submits a task to the routing system 104 via the communication network 108 using a computing device, a telephone, a mobile phone, a personal digital assistant, or the like.
- the individual 102 is anyone who submits a task to the routing system 104 and may be, for example, a person, someone acting on behalf of an entity, or a group of people.
- the routing system 104 is configured to receive the task from the individual 102 .
- the task includes at least one task attribute that quantitatively describes an identified skill or skill set that is required to complete the task and is associated with a value indicating the relative importance of the skill.
- the routing system 104 may comprise one or more computing devices including computer readable media, a processor, and/or logic.
- the routing system 104 may comprise a processor configured to execute computing instructions stored in the computer readable medium. These instructions may be embodied in software.
- the computer readable medium comprises an IC memory chip, such as, for example, static random access memory (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), non-volatile random access memory (NVRAM), and read only memory (ROM), such as erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), and flash memory.
- SRAM static random access memory
- DRAM dynamic random access memory
- SDRAM synchronous DRAM
- NVRAM non-volatile random access memory
- ROM read only memory
- EPROM erasable programmable ROM
- EEPROM electrically erasable programmable ROM
- the routing system 104 may comprise one or more chips with logic circuitry, such as, for example, an application specific integrated circuit (ASIC), a processor, a microprocessor, a microcontroller, a field programmable gate array (FPGA), a programmable logic device (PLD), a complex programmable logic device (CPLD), or other logic device.
- ASIC application specific integrated circuit
- FPGA field programmable gate array
- PLD programmable logic device
- CPLD complex programmable logic device
- the routing system 104 is configured to receive the task. Upon receiving the task, the routing system 104 is configured to access a plurality of profiles of users 106 and determine to which user of the plurality of users 106 to allocate the task.
- Each user of the users 106 is associated with a profile.
- a user is a person who may be able to provide a solution to a task.
- the profile may include information about the user's skills, the user's behavior in response to previous tasks, and/or demographic information. The profiles are discussed further in connection with FIG. 2 .
- the routing system 104 Upon allocating the task to a user, the routing system 104 routes a message describing the task to the user via the communication network 108 using a computing device, a telephone, a mobile phone, a personal digital assistant, or the like. The user may in turn provide a solution to the task. Feedback is collected by the routing system 104 based at least on whether the user provided a solution to the task or if the individual accepted the solution to the task.
- the communications network 108 includes, but is not limited to, a local area network (LAN), a wide area network (WAN), the Internet, a telephone network, or the like.
- FIG. 2 is a block diagram of the routing system 104 for allocating a task to a user according to various embodiments.
- the routing system 104 includes an input module 202 , a profile module 204 , a supervised learning module 206 , an unsupervised learning module 208 , a stochastic module 210 , an output module 212 , and a feedback module 214 .
- the modules may be implemented in the routing system 104 as software and/or hardware.
- the input module 202 is configured to receive a task including a set of normalized task attributes.
- Each task attribute includes an identifier of a particular skill and a normalized quantifier indicating the importance of the skill for providing a solution to the task.
- the task attribute tree 300 is an organization of the task attributes and corresponds to the various skills of the users, for example, users 106 .
- the task is a support request 302 for a mobile communications provider.
- the individual e.g., individual 102 , initiating the task may identify the task as a technical task 304 .
- the technical task 304 is assigned an intermediate value 306 of twenty. If the individual can not further define the task, the intermediate value is assigned to all the leaves depending from the technical task 304 . Otherwise, the individual may specify that the technical task 304 is a services task 308 having an intermediate value 310 of twenty.
- the task is further identified as an SMS task 312 .
- the SMS task is associated with a skill value 314 of twenty.
- the skill value 314 may be based on a template used to define the task, be specified by the individual, or be based on a questionnaire answered by the user, for example.
- the intermediate values 306 and 310 may have a value other than twenty.
- the skill value 314 may be a different value.
- the task attribute tree 300 is shown for the purposes of illustration only. As will be apparent to those skilled in the art, other data structures can be used to assign task attributes.
- the task attribute values, once determined, are normalized according to known normalization techniques.
- the profile module 204 is configured to access profiles of users.
- a profile For each user, a profile includes a plurality of skill metrics and a plurality of performance indicators.
- the profile also includes demographic information about the user such as age, gender, location, political affiliation, or the like.
- the skill metrics correspond to the particular skills defined by the task attributes and indicate the user's skill level for each particular skill.
- the skill metrics are determined based on formal training of the user such as certifications, training sessions, seminars, coursework, and the like.
- the data in the profile may be imported from another system or platform.
- the skill metrics are also based on whether a previous solution provided by the user that required the skill was accepted.
- the table 350 includes skills 352 which correspond to the task attributes of the task attribute tree 300 .
- the table 350 includes the skill metrics for each task attribute of User A 354 , User B 356 , User C 358 , and User D 360 .
- the performance indicators within the profile module 204 include behavioral factors of a user. Typically, the performance indicators are cumulatively calculated as a user is allocated tasks and provides solutions to those tasks. To illustrate, one performance indicator is “reliability” which is measured by dividing the number of tasks that the user has solved by the number of tasks that the user has been allocated. Another example is “commitment” which is measured by dividing the number of tasks that the user has accepted from the routing system 104 by the number of tasks that the user has been allocated. Further performance indicators may be measured based on, for example, an amount of time to accept an allocated task or an amount of time to provide a solution to the allocated task.
- the supervised learning module 206 is configured to calculate at least one initial value for each profile based on the at least one normalized task attribute and the plurality of skill metrics. For each task attribute associated with the task, an initial value is generated based on the value of a corresponding skill metric within the profile of the user. The initial value may be zero for a task attribute if the user has a skill metric having a zero value or if the individual indicated that task attribute was not is not relevant to the current task.
- the unsupervised learning module 208 is configured to calculate a fitness metric for each profile based on the initial values.
- the unsupervised learning module 208 uses a neural network for each profile.
- the initial values for each profile are input into the unsupervised learning module 208 .
- the unsupervised learning module 208 uses a neural network that includes weighting factors that are based on the plurality of performance indicators within the profile. Neural networks are generally known to those skilled in the art.
- the neural network 400 includes a supervised learning portion 402 and an unsupervised learning portion 404 .
- the supervised learning portion 402 includes normalized task attributes A N 406 and skill metrics Sk N 408 both as described in connection with FIG. 3A .
- the supervised learning portion 402 is implemented by the supervised learning module 206 .
- the unsupervised learning portion 404 implemented by the unsupervised learning module 208 , includes a neural network having weights w m 412 between nodes.
- the unsupervised learning portion 404 receives the initial values 410 and calculates a fitness metric 414 .
- FIG. 5A depicts a neural network 500 associated with the User A 354 of FIG. 3B according to various embodiments.
- the normalized task attributes 502 for the SMS task as described in connection with FIG. 3A are input into the supervised learning portion 402 .
- the skill metrics 504 of User A 354 as described in connection with FIG. 3B are also included in the supervised learning portion of the neural network 500 .
- the initial values 506 are calculated as discussed in connection with FIG. 4 .
- an unsupervised learning portion 404 of the neural network 500 calculates a fitness metric 508 of 4800 for the User A 354 .
- the weights in the neural network 500 are all depicted as the value two; however, as will be apparent to one skilled in the art, the weights may vary.
- FIG. 5B is a depiction of a neural network 530 associated with the User B 356 of FIG. 3B according to various embodiments.
- the neural network 530 receives the same task attributes 502 as the neural network 500 .
- the supervised learning portion 402 of the neural network 530 includes the skill metrics 532 of User B 356 .
- the unsupervised learning portion 404 of the neural network 530 calculates a fitness metric 536 of 1920 for the User B 356 .
- FIG. 5C is a depiction of a neural network 560 associated with the User C 358 of FIG. 3B according to various embodiments.
- the neural network 560 receives the same task attributes 502 as the neural network 500 .
- the supervised learning portion 402 of the neural network 560 includes the skill metrics 562 of User C 358 .
- the unsupervised learning portion 404 of the neural network 560 calculates a fitness value 566 of 960 for the User C 358 .
- FIG. 5D is a depiction of a neural network 590 associated with the User D 360 of FIG. 3B according to various embodiments.
- the neural network 590 receives the same task attributes 502 as the neural network 500 .
- the supervised learning portion 402 of the neural network 590 includes the skill metrics 592 of User D 360 .
- the unsupervised learning portion 404 of the neural network 590 calculates a fitness metric 596 of 240 for the User D 360 .
- the stochastic module 210 receives the fitness metrics calculated by the unsupervised learning module 208 .
- the stochastic module 210 is configured to select a profile by implementing a stochastic model using the fitness metric. Because neural networks are designed to select a single best pathway based on feedback, a neural network by itself consistently routes similar tasks to the same user. To avoid allocating too many tasks to a single user, the stochastic module 210 is included in the routing system 104 .
- the stochastic module 210 randomly selects profiles associated with a fitness metric in order to distribute tasks among users who would not otherwise be selected because another user has a higher fitness metric for a specific task.
- FIG. 6 is a depiction of a selection of a user according to various embodiments.
- a list of the profiles is sorted according to fitness value.
- the table 602 includes a sorted list of the fitness metric 508 of User A 354 from neural network 500 as described in FIG. 5A , the fitness metric 536 of User B 356 from neural network 530 as described in FIG. 5B , the fitness metric 566 of User C 358 from neural network 560 as described in FIG. 5C , and the fitness metric 596 of User D 360 from neural network 590 as described in FIG. 5D .
- a portion 604 of the profiles may be pre-selected based on a threshold fitness metric, a percentile threshold, or the like.
- the portion 604 is based on a percentile threshold which pre-selects the profiles having a fitness metric within a top 50% percentile of the fitness metrics.
- the stochastic model 606 may select a profile based on a Gaussian distribution, a lottery, or the like.
- each profile is assigned a range of numbers according to a probability of being selected based on the fitness metric.
- a random number is generated.
- the profile assigned to range that includes the random number is selected for the task.
- User A 354 and User B 356 may be pre-selected because both are associated with a fitness metric in the top fiftieth percentile.
- the profile of User A 354 has a 71% chance of being selected and the profile of User B 356 has a 29% chance of being selected.
- the profile of User A 354 is assigned a range of numbers from zero to 0.71 and the profile of User B 356 is assigned a range of numbers from 0.72 to 1.00.
- a random number is generated. If the random number is 0.89, the task is allocated to the profile of User B 356 even though User B 356 has a lower fitness metric than User A 354 .
- the output module 212 is configured to route the task to the user associated with the selected profile.
- the feedback module 214 is configured to collect feedback from at least two sources.
- the first source of feedback is based on the behavior of the user in performing the task. For example, data may be collected based on whether the user accepts the task, an elapsed amount of time for the selected user to accept the task, whether the user provided a solution to the task, an elapsed amount of time for the user to provide a solution to the task, a number of times that the user has completed a previous task, whether the selected user has accepted a task within a time-out period, whether the selected user has provided a solution to the task within a time-out period, or the like.
- the second source of feedback is the individual who initiated the task. This feedback includes accepting or rejecting the solution provided by the user.
- the feedback module 212 is configured to update the profile of the user based on the feedback. For example, if the solution is accepted, the skill metrics associated with the task attributes may be increased or weights within the neural network may be modified as is discussed in connection with FIG. 7 . The feedback based on the behavior of the user may be used to update weights within the neural network according to a learning function.
- an updated neural network 700 associated with the User B 356 is depicted.
- the skill metric 702 associated with the SMS skill is increased from the value forty to the value forty-one.
- the skill metric may be modified by incrementing the previous skill level by a pre-defined amount for each solution provided by the user that is accepted. Other methods of updating the skill metric will be apparent to those skilled in the art.
- the unsupervised portion 404 is updated by modifying the weights and/or the topology of the neural network 700 according to known methods.
- new weights 704 , 706 , and 708 are calculated along one possible path through the neural network. In this example, because each possible path in the neural network 530 of FIG. 5B has equal weights, the selection of the weights that are modified is random. In some embodiments, only the most heavily weighted path is updated.
- the routing system 104 as described in connection with FIGS. 1-7 therefore allows tasks to be allocated to users based on known information stored as skill metrics and behavioral information stored as performance indicators. As each user is allocated tasks and provides solutions to those tasks, the neural networks become more refined and are more able to accurately measure fitness values associated with each user. Further, the stochastic model allows for distribution of the tasks to users who may be able to provide a solution to the task but would not have been chosen by merely comparing fitness metrics calculated by the neural networks.
- FIG. 8 is a flowchart of a method 800 for allocating a task to a user according to various embodiments.
- the method 800 may be performed by the routing system 104 .
- a task is received.
- the task includes at least one normalized skill attribute.
- the step 802 may be performed by the input module 202 .
- step 804 profiles are accessed. Each profile is associated with a user and includes skill metrics and performance indicators. The step 804 may be performed by the profile module 204 .
- Initial values for each profile are calculated in step 806 .
- the initial values may be calculated by the supervised learning module 206 .
- a fitness metric for each profile is calculated in step 808 .
- the step 808 may be performed by the unsupervised learning module 208 .
- a profile is selected by, for example, the stochastic module 210 .
- step 812 the task is routed to the user associated with the selected profile by, for example, the output module 212 .
- step 814 feedback is collected from the individual who initiated the task and/or based on the behavior of the user associated with the selected profile.
- the feedback may be collected by the feedback module 214 .
- SMS task example 312 has been discussed in detail for the purposes of illustration, as will be apparent to those skilled in the art, other types of tasks may be allocated using the routing system 104 .
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Abstract
Description
I N =A N *Sk N
While the calculation of the initial values is shown as part of the neural network, it will be apparent that other techniques may be used. The
Σ=Σ(I N *w m)
As is apparent to those skilled in neural networks, at each successive node, the value is calculated in similar fashion. It should be noted that each
w m(new) =w m +s/n
where wm(new) is the updated weight between the designated nodes, wm is the previous weight between the designated nodes, s is +1 if the solution was accepted and −1 if the solution was rejected, and n is the incoming connections on the node. Using this equation,
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Cited By (22)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20060247959A1 (en) * | 2005-04-29 | 2006-11-02 | Tracy Oden | System and method for provisioning, fulfilling, and delivering full service information technology, management and other professional services and ancillary consulting support in real time via an integrated technology architecture while enabling end clients to procure, transact and receive these services and associated work products, on demand, in a just-in-time (JIT) fashion. |
US20120084108A1 (en) * | 2010-09-30 | 2012-04-05 | Adc Telecommunications, Inc. | Systems and methods for a work flow management application suite for mobile communications devices |
US20120095925A1 (en) * | 2010-10-15 | 2012-04-19 | Invensys Systems Inc. | System and Method of Federated Workflow Data Storage |
US20130138461A1 (en) * | 2011-11-30 | 2013-05-30 | At&T Intellectual Property I, L.P. | Mobile Service Platform |
US8554605B2 (en) | 2011-06-29 | 2013-10-08 | CrowdFlower, Inc. | Evaluating a worker in performing crowd sourced tasks and providing in-task training through programmatically generated test tasks |
US8626545B2 (en) | 2011-10-17 | 2014-01-07 | CrowdFlower, Inc. | Predicting future performance of multiple workers on crowdsourcing tasks and selecting repeated crowdsourcing workers |
US20140149513A1 (en) * | 2012-11-23 | 2014-05-29 | The Extraordinaries, Inc. | System and method for matching a profile to a sparsely defined request |
WO2014107517A1 (en) * | 2013-01-02 | 2014-07-10 | E-Rewards, Inc. | Priority-weighted quota cell selection to match a panelist to a market research project |
US9141924B2 (en) | 2012-02-17 | 2015-09-22 | International Business Machines Corporation | Generating recommendations for staffing a project team |
US20160071048A1 (en) * | 2014-09-08 | 2016-03-10 | Xerox Corporation | Methods and systems for crowdsourcing of tasks |
US9390195B2 (en) | 2013-01-02 | 2016-07-12 | Research Now Group, Inc. | Using a graph database to match entities by evaluating boolean expressions |
US9407510B2 (en) | 2013-09-04 | 2016-08-02 | Commscope Technologies Llc | Physical layer system with support for multiple active work orders and/or multiple active technicians |
US9403482B2 (en) | 2013-11-22 | 2016-08-02 | At&T Intellectual Property I, L.P. | Enhanced view for connected cars |
US9436738B2 (en) | 2012-04-19 | 2016-09-06 | Nant Holdings Ip, Llc | Mechanical Turk integrated IDE, systems and method |
US10013481B2 (en) | 2013-01-02 | 2018-07-03 | Research Now Group, Inc. | Using a graph database to match entities by evaluating boolean expressions |
US10223699B2 (en) | 2014-08-05 | 2019-03-05 | CrowdCare Corporation | System and method of rule creation based on frequency of question |
US10853744B2 (en) | 2010-06-17 | 2020-12-01 | Figure Eight Technologies, Inc. | Distributing a task to multiple workers over a network for completion while providing quality control |
US11023859B2 (en) | 2010-06-17 | 2021-06-01 | CrowdFlower, Inc. | Using virtual currency to compensate workers in a crowdsourced task |
US11087247B2 (en) | 2011-03-23 | 2021-08-10 | Figure Eight Technologies, Inc. | Dynamic optimization for data quality control in crowd sourcing tasks to crowd labor |
US11113642B2 (en) | 2012-09-27 | 2021-09-07 | Commscope Connectivity Uk Limited | Mobile application for assisting a technician in carrying out an electronic work order |
US11210566B2 (en) * | 2018-09-27 | 2021-12-28 | Kabushiki Kaisha Toshiba | Training apparatus, training method and recording medium |
US11568334B2 (en) | 2012-03-01 | 2023-01-31 | Figure Eight Technologies, Inc. | Adaptive workflow definition of crowd sourced tasks and quality control mechanisms for multiple business applications |
Families Citing this family (13)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20100211428A1 (en) * | 2009-02-18 | 2010-08-19 | Red Hat, Inc. | Automated Customer Service Matching Methodology |
US9378511B2 (en) | 2009-07-15 | 2016-06-28 | International Business Machines Corporation | Real-time appointment of enterprise mobile agents in response to customer requests |
US8751879B2 (en) | 2011-03-30 | 2014-06-10 | International Business Machines Corporation | Intelligently monitoring and dispatching information technology service alerts |
US20120284090A1 (en) * | 2011-05-02 | 2012-11-08 | Sergejs Marins | System and method for accumulation and verification of trust for participating users in a crowd sourcing activity |
US9231895B2 (en) * | 2012-10-23 | 2016-01-05 | International Business Machines Corporation | Tag management of information technology services improvement |
US9591052B2 (en) | 2013-02-05 | 2017-03-07 | Apple Inc. | System and method for providing a content distribution network with data quality monitoring and management |
US20150213393A1 (en) * | 2014-01-27 | 2015-07-30 | Xerox Corporation | Methods and systems for presenting task information to crowdworkers |
US20150269586A1 (en) * | 2014-03-18 | 2015-09-24 | Cellco Partnership D/B/A Verizon Wireless | Method and System for Crowd-Sourcing Customer Care |
AU2018201691B2 (en) | 2017-03-10 | 2018-12-06 | Accenture Global Solutions Limited | Job allocation |
US11036938B2 (en) * | 2017-10-20 | 2021-06-15 | ConceptDrop Inc. | Machine learning system for optimizing projects |
US10776166B2 (en) * | 2018-04-12 | 2020-09-15 | Vmware, Inc. | Methods and systems to proactively manage usage of computational resources of a distributed computing system |
WO2021215906A1 (en) * | 2020-04-24 | 2021-10-28 | Samantaray Shubhabrata | Artificial intelligence-based method for analysing raw data |
EP4309102A1 (en) * | 2021-03-17 | 2024-01-24 | Hitachi Vantara LLC | Data driven approaches for performance-based project management |
Citations (50)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5164897A (en) * | 1989-06-21 | 1992-11-17 | Techpower, Inc. | Automated method for selecting personnel matched to job criteria |
JPH05225203A (en) * | 1992-02-17 | 1993-09-03 | Nippon Telegr & Teleph Corp <Ntt> | System for resolving job shop scheduling problem |
US5416694A (en) * | 1994-02-28 | 1995-05-16 | Hughes Training, Inc. | Computer-based data integration and management process for workforce planning and occupational readjustment |
JPH07282144A (en) * | 1994-04-12 | 1995-10-27 | Nippon Telegr & Teleph Corp <Ntt> | Job shop scheduling device |
JPH08110920A (en) * | 1994-10-12 | 1996-04-30 | Nippon Telegr & Teleph Corp <Ntt> | Job shop scheduling method |
JPH09282359A (en) * | 1996-04-09 | 1997-10-31 | Nippon Telegr & Teleph Corp <Ntt> | Job-shop scheduling device |
US5974392A (en) * | 1995-02-14 | 1999-10-26 | Kabushiki Kaisha Toshiba | Work flow system for task allocation and reallocation |
US6049776A (en) * | 1997-09-06 | 2000-04-11 | Unisys Corporation | Human resource management system for staffing projects |
US6272467B1 (en) * | 1996-09-09 | 2001-08-07 | Spark Network Services, Inc. | System for data collection and matching compatible profiles |
US6275812B1 (en) * | 1998-12-08 | 2001-08-14 | Lucent Technologies, Inc. | Intelligent system for dynamic resource management |
US6289340B1 (en) * | 1999-08-03 | 2001-09-11 | Ixmatch, Inc. | Consultant matching system and method for selecting candidates from a candidate pool by adjusting skill values |
US20010042000A1 (en) * | 1998-11-09 | 2001-11-15 | William Defoor | Method for matching job candidates with employers |
JP2001338097A (en) * | 2000-01-25 | 2001-12-07 | Satoshi Hashimoto | Expert retrieving system and product information distribution system and management supporting system and management support information distribution system |
US20020002479A1 (en) * | 1999-12-20 | 2002-01-03 | Gal Almog | Career management system |
US20020055870A1 (en) * | 2000-06-08 | 2002-05-09 | Thomas Roland R. | System for human capital management |
US20030033185A1 (en) * | 2001-07-26 | 2003-02-13 | Leto Kevin R. | Method for matching a user to a subscriber |
US20030105657A1 (en) * | 2001-11-16 | 2003-06-05 | Nandigama Murali K. | Personal resource management tool |
US6578005B1 (en) * | 1996-11-22 | 2003-06-10 | British Telecommunications Public Limited Company | Method and apparatus for resource allocation when schedule changes are incorporated in real time |
WO2003081871A1 (en) | 2002-03-27 | 2003-10-02 | First Hop Ltd. | System and method for managing messaging services |
US20030191680A1 (en) * | 2000-06-12 | 2003-10-09 | Dewar Katrina L. | Computer-implemented system for human resources management |
US6662194B1 (en) * | 1999-07-31 | 2003-12-09 | Raymond Anthony Joao | Apparatus and method for providing recruitment information |
US20040010480A1 (en) * | 2002-07-09 | 2004-01-15 | Lalitha Agnihotri | Method, apparatus, and program for evolving neural network architectures to detect content in media information |
JP2004118648A (en) * | 2002-09-27 | 2004-04-15 | Toshiba Microelectronics Corp | Resource management server, human resource management method and human resource management system |
US6735570B1 (en) * | 1999-08-02 | 2004-05-11 | Unisys Corporation | System and method for evaluating a selectable group of people against a selectable set of skills |
US6823315B1 (en) * | 1999-11-03 | 2004-11-23 | Kronos Technology Systems Limited Partnership | Dynamic workforce scheduler |
US20050202380A1 (en) * | 2004-02-27 | 2005-09-15 | Vitalknot | Personnel evaluation method, personnel evaluation system, personnel evaluation information processing unit, and personnel evaluation program |
US20050246299A1 (en) * | 2000-08-03 | 2005-11-03 | Unicru, Inc. | Electronic employee selection systems and methods |
JP2005327028A (en) * | 2004-05-13 | 2005-11-24 | Ricoh Co Ltd | Talent search system, program, and recording medium |
US20050261953A1 (en) * | 2004-05-24 | 2005-11-24 | Malek Kamal M | Determining design preferences of a group |
US20060106675A1 (en) * | 2004-11-16 | 2006-05-18 | Cohen Peter D | Providing an electronic marketplace to facilitate human performance of programmatically submitted tasks |
US20060106774A1 (en) * | 2004-11-16 | 2006-05-18 | Cohen Peter D | Using qualifications of users to facilitate user performance of tasks |
JP2006244000A (en) * | 2005-03-02 | 2006-09-14 | Toshiba Corp | Process management device and process management program |
US20060229902A1 (en) * | 2005-04-11 | 2006-10-12 | Mcgovern Robert J | Match-based employment system and method |
US20060271421A1 (en) * | 2005-05-03 | 2006-11-30 | Dan Steneker | Computer-aided system and method for visualizing and quantifying candidate preparedness for specific job roles |
US7149703B2 (en) * | 2001-04-19 | 2006-12-12 | Accolo, Inc. | Method and system for generating referrals for job positions based upon virtual communities comprised of members relevant to the job positions |
US7167855B1 (en) * | 1999-10-15 | 2007-01-23 | Richard Koenig | Internet-based matching service for expert consultants and customers with matching of qualifications and times of availability |
US7191139B2 (en) * | 2000-04-15 | 2007-03-13 | Mindloft Corporation | System for cataloging, inventorying, selecting, measuring, valuing and matching intellectual capital and skills with a skill requirement |
US7212985B2 (en) * | 2000-10-10 | 2007-05-01 | Intragroup, Inc. | Automated system and method for managing a process for the shopping and selection of human entities |
US20080082542A1 (en) * | 2006-09-29 | 2008-04-03 | Cohen Peter D | Facilitating performance of tasks via distribution using third-party sites |
US20080208849A1 (en) * | 2005-12-23 | 2008-08-28 | Conwell William Y | Methods for Identifying Audio or Video Content |
US20080228549A1 (en) * | 2007-03-14 | 2008-09-18 | Harrison Michael J | Performance evaluation systems and methods |
US7437309B2 (en) * | 2001-02-22 | 2008-10-14 | Corporate Fables, Inc. | Talent management system and methods for reviewing and qualifying a workforce utilizing categorized and free-form text data |
US7502748B1 (en) * | 1999-08-31 | 2009-03-10 | Careerious Inc. | Job matching system and method |
US7593860B2 (en) * | 2005-09-12 | 2009-09-22 | International Business Machines Corporation | Career analysis method and system |
US7660723B2 (en) * | 2006-11-17 | 2010-02-09 | International Business Machines Corporation | Ranking method and system |
US7698235B2 (en) * | 2003-09-29 | 2010-04-13 | Nec Corporation | Ensemble learning system and method |
US7720791B2 (en) * | 2005-05-23 | 2010-05-18 | Yahoo! Inc. | Intelligent job matching system and method including preference ranking |
US7805382B2 (en) * | 2005-04-11 | 2010-09-28 | Mkt10, Inc. | Match-based employment system and method |
US7881957B1 (en) * | 2004-11-16 | 2011-02-01 | Amazon Technologies, Inc. | Identifying tasks for task performers based on task subscriptions |
US7885844B1 (en) * | 2004-11-16 | 2011-02-08 | Amazon Technologies, Inc. | Automatically generating task recommendations for human task performers |
-
2008
- 2008-02-25 US US12/072,382 patent/US8099311B2/en not_active Expired - Fee Related
- 2008-02-25 WO PCT/IB2008/000410 patent/WO2008102255A1/en active Application Filing
Patent Citations (51)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5164897A (en) * | 1989-06-21 | 1992-11-17 | Techpower, Inc. | Automated method for selecting personnel matched to job criteria |
JPH05225203A (en) * | 1992-02-17 | 1993-09-03 | Nippon Telegr & Teleph Corp <Ntt> | System for resolving job shop scheduling problem |
US5416694A (en) * | 1994-02-28 | 1995-05-16 | Hughes Training, Inc. | Computer-based data integration and management process for workforce planning and occupational readjustment |
JPH07282144A (en) * | 1994-04-12 | 1995-10-27 | Nippon Telegr & Teleph Corp <Ntt> | Job shop scheduling device |
JPH08110920A (en) * | 1994-10-12 | 1996-04-30 | Nippon Telegr & Teleph Corp <Ntt> | Job shop scheduling method |
US5974392A (en) * | 1995-02-14 | 1999-10-26 | Kabushiki Kaisha Toshiba | Work flow system for task allocation and reallocation |
JPH09282359A (en) * | 1996-04-09 | 1997-10-31 | Nippon Telegr & Teleph Corp <Ntt> | Job-shop scheduling device |
US6272467B1 (en) * | 1996-09-09 | 2001-08-07 | Spark Network Services, Inc. | System for data collection and matching compatible profiles |
US6578005B1 (en) * | 1996-11-22 | 2003-06-10 | British Telecommunications Public Limited Company | Method and apparatus for resource allocation when schedule changes are incorporated in real time |
US6049776A (en) * | 1997-09-06 | 2000-04-11 | Unisys Corporation | Human resource management system for staffing projects |
US20010042000A1 (en) * | 1998-11-09 | 2001-11-15 | William Defoor | Method for matching job candidates with employers |
US6275812B1 (en) * | 1998-12-08 | 2001-08-14 | Lucent Technologies, Inc. | Intelligent system for dynamic resource management |
US6662194B1 (en) * | 1999-07-31 | 2003-12-09 | Raymond Anthony Joao | Apparatus and method for providing recruitment information |
US6735570B1 (en) * | 1999-08-02 | 2004-05-11 | Unisys Corporation | System and method for evaluating a selectable group of people against a selectable set of skills |
US6289340B1 (en) * | 1999-08-03 | 2001-09-11 | Ixmatch, Inc. | Consultant matching system and method for selecting candidates from a candidate pool by adjusting skill values |
US7502748B1 (en) * | 1999-08-31 | 2009-03-10 | Careerious Inc. | Job matching system and method |
US7167855B1 (en) * | 1999-10-15 | 2007-01-23 | Richard Koenig | Internet-based matching service for expert consultants and customers with matching of qualifications and times of availability |
US6823315B1 (en) * | 1999-11-03 | 2004-11-23 | Kronos Technology Systems Limited Partnership | Dynamic workforce scheduler |
US20020002479A1 (en) * | 1999-12-20 | 2002-01-03 | Gal Almog | Career management system |
JP2001338097A (en) * | 2000-01-25 | 2001-12-07 | Satoshi Hashimoto | Expert retrieving system and product information distribution system and management supporting system and management support information distribution system |
US7191139B2 (en) * | 2000-04-15 | 2007-03-13 | Mindloft Corporation | System for cataloging, inventorying, selecting, measuring, valuing and matching intellectual capital and skills with a skill requirement |
US7191138B1 (en) * | 2000-04-15 | 2007-03-13 | Mindloft Corporation | System for cataloging, inventorying selecting, measuring, valuing and matching intellectual capital and skills with a skill requirement |
US20020055870A1 (en) * | 2000-06-08 | 2002-05-09 | Thomas Roland R. | System for human capital management |
US20030191680A1 (en) * | 2000-06-12 | 2003-10-09 | Dewar Katrina L. | Computer-implemented system for human resources management |
US20050246299A1 (en) * | 2000-08-03 | 2005-11-03 | Unicru, Inc. | Electronic employee selection systems and methods |
US7212985B2 (en) * | 2000-10-10 | 2007-05-01 | Intragroup, Inc. | Automated system and method for managing a process for the shopping and selection of human entities |
US7437309B2 (en) * | 2001-02-22 | 2008-10-14 | Corporate Fables, Inc. | Talent management system and methods for reviewing and qualifying a workforce utilizing categorized and free-form text data |
US7149703B2 (en) * | 2001-04-19 | 2006-12-12 | Accolo, Inc. | Method and system for generating referrals for job positions based upon virtual communities comprised of members relevant to the job positions |
US20030033185A1 (en) * | 2001-07-26 | 2003-02-13 | Leto Kevin R. | Method for matching a user to a subscriber |
US20030105657A1 (en) * | 2001-11-16 | 2003-06-05 | Nandigama Murali K. | Personal resource management tool |
WO2003081871A1 (en) | 2002-03-27 | 2003-10-02 | First Hop Ltd. | System and method for managing messaging services |
US20040010480A1 (en) * | 2002-07-09 | 2004-01-15 | Lalitha Agnihotri | Method, apparatus, and program for evolving neural network architectures to detect content in media information |
JP2004118648A (en) * | 2002-09-27 | 2004-04-15 | Toshiba Microelectronics Corp | Resource management server, human resource management method and human resource management system |
US7698235B2 (en) * | 2003-09-29 | 2010-04-13 | Nec Corporation | Ensemble learning system and method |
US20050202380A1 (en) * | 2004-02-27 | 2005-09-15 | Vitalknot | Personnel evaluation method, personnel evaluation system, personnel evaluation information processing unit, and personnel evaluation program |
JP2005327028A (en) * | 2004-05-13 | 2005-11-24 | Ricoh Co Ltd | Talent search system, program, and recording medium |
US20050261953A1 (en) * | 2004-05-24 | 2005-11-24 | Malek Kamal M | Determining design preferences of a group |
US20060106774A1 (en) * | 2004-11-16 | 2006-05-18 | Cohen Peter D | Using qualifications of users to facilitate user performance of tasks |
US20060106675A1 (en) * | 2004-11-16 | 2006-05-18 | Cohen Peter D | Providing an electronic marketplace to facilitate human performance of programmatically submitted tasks |
US7885844B1 (en) * | 2004-11-16 | 2011-02-08 | Amazon Technologies, Inc. | Automatically generating task recommendations for human task performers |
US7881957B1 (en) * | 2004-11-16 | 2011-02-01 | Amazon Technologies, Inc. | Identifying tasks for task performers based on task subscriptions |
JP2006244000A (en) * | 2005-03-02 | 2006-09-14 | Toshiba Corp | Process management device and process management program |
US7805382B2 (en) * | 2005-04-11 | 2010-09-28 | Mkt10, Inc. | Match-based employment system and method |
US20060229902A1 (en) * | 2005-04-11 | 2006-10-12 | Mcgovern Robert J | Match-based employment system and method |
US20060271421A1 (en) * | 2005-05-03 | 2006-11-30 | Dan Steneker | Computer-aided system and method for visualizing and quantifying candidate preparedness for specific job roles |
US7720791B2 (en) * | 2005-05-23 | 2010-05-18 | Yahoo! Inc. | Intelligent job matching system and method including preference ranking |
US7593860B2 (en) * | 2005-09-12 | 2009-09-22 | International Business Machines Corporation | Career analysis method and system |
US20080208849A1 (en) * | 2005-12-23 | 2008-08-28 | Conwell William Y | Methods for Identifying Audio or Video Content |
US20080082542A1 (en) * | 2006-09-29 | 2008-04-03 | Cohen Peter D | Facilitating performance of tasks via distribution using third-party sites |
US7660723B2 (en) * | 2006-11-17 | 2010-02-09 | International Business Machines Corporation | Ranking method and system |
US20080228549A1 (en) * | 2007-03-14 | 2008-09-18 | Harrison Michael J | Performance evaluation systems and methods |
Non-Patent Citations (19)
Title |
---|
Amazon Web Services: Amazon Mechanical Turk; http://www.amazon.com/Mechanical-Turk-AWS-home-page/b/ref, 2008. |
Aspect Contact Servers (Aspect Product Overview), Aspect Software, 300 Apollo Drive, Chelmsford, MA 01824; www.aspect.com. |
Cambrian House, Home of Crowdsourcing; http://www.cambrianhouse.com/. |
Clickworkers; http://clickworkers.arc.nasa.gov/top. |
Crowdsourcing-Wikipedia; http://en.wikipedia.org/wiki/Crowdsourcing. |
Don Tapscott and Anthony D. Williams; Innovation in the Age of Mass Collarboration; Businessweek, Feb. 1, 2007; http://www.businessweek.com. |
Elance-Outsourcing to freelance programmers, web & logo design; http://www.elance.com/p/landing/buyer.html, 2008. |
Hollingsworth, David; Workflow Management Coalition, "The Workflow Reference Model," Jan. 19, 1995; p. 13. * |
HumanGrid: Human-powered data services; HumanGrid GmbH; http://www.humangrid.eu/. |
InnoCentive Open Innovation Marketplace; http://www.innocentive.com, 2007. |
iStockPhoto; http://www.istockphoto.com/index.php, 2008. |
Jeff Howe,"The Rise of Crowdsourcing," Wired, Jun. 2006; http://www.wired.com/wired/archive/14.06/crowds.html. |
JeffPHowe; Crowdsourcing; Wired Magazine, May 27, 2008; http://crowdsourcing.typepad.com/. |
Kluster-real-world group decision-making; http://kluster.com. |
Paul Boutin; Crowdsourcing: Consumers as Creators; Businessweek; Jul. 13, 2006; http://www.businessweek.com. |
Pipes: Rewire the Web; http://pipes.yahoo.com/pipes/, 2008. |
Workflow Reference Model Diagram; http://www.wfmc.org/standards/referencemodel.html. |
Yahoo! Answers blog; http://answers.yahoo.com/, 2008. |
yourEncore; http://www.yourencore.com/ , 2008. |
Cited By (29)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20060247959A1 (en) * | 2005-04-29 | 2006-11-02 | Tracy Oden | System and method for provisioning, fulfilling, and delivering full service information technology, management and other professional services and ancillary consulting support in real time via an integrated technology architecture while enabling end clients to procure, transact and receive these services and associated work products, on demand, in a just-in-time (JIT) fashion. |
US8849685B2 (en) * | 2005-04-29 | 2014-09-30 | Tracy Denise Oden | System for real-time on-demand provisioning, fulfilling, and delivering full service professional services |
US10853744B2 (en) | 2010-06-17 | 2020-12-01 | Figure Eight Technologies, Inc. | Distributing a task to multiple workers over a network for completion while providing quality control |
US11023859B2 (en) | 2010-06-17 | 2021-06-01 | CrowdFlower, Inc. | Using virtual currency to compensate workers in a crowdsourced task |
US20120084108A1 (en) * | 2010-09-30 | 2012-04-05 | Adc Telecommunications, Inc. | Systems and methods for a work flow management application suite for mobile communications devices |
US20120095925A1 (en) * | 2010-10-15 | 2012-04-19 | Invensys Systems Inc. | System and Method of Federated Workflow Data Storage |
US11087247B2 (en) | 2011-03-23 | 2021-08-10 | Figure Eight Technologies, Inc. | Dynamic optimization for data quality control in crowd sourcing tasks to crowd labor |
US8554605B2 (en) | 2011-06-29 | 2013-10-08 | CrowdFlower, Inc. | Evaluating a worker in performing crowd sourced tasks and providing in-task training through programmatically generated test tasks |
US8626545B2 (en) | 2011-10-17 | 2014-01-07 | CrowdFlower, Inc. | Predicting future performance of multiple workers on crowdsourcing tasks and selecting repeated crowdsourcing workers |
US20130138461A1 (en) * | 2011-11-30 | 2013-05-30 | At&T Intellectual Property I, L.P. | Mobile Service Platform |
US9875448B2 (en) * | 2011-11-30 | 2018-01-23 | At&T Intellectual Property I, L.P. | Mobile service platform |
US10963822B2 (en) | 2011-11-30 | 2021-03-30 | At&T Intellectual Property I, L.P. | Mobile service platform |
US9141924B2 (en) | 2012-02-17 | 2015-09-22 | International Business Machines Corporation | Generating recommendations for staffing a project team |
US11568334B2 (en) | 2012-03-01 | 2023-01-31 | Figure Eight Technologies, Inc. | Adaptive workflow definition of crowd sourced tasks and quality control mechanisms for multiple business applications |
US10762430B2 (en) * | 2012-04-19 | 2020-09-01 | Nant Holdings Ip, Llc | Mechanical turk integrated ide, systems and method |
US9436738B2 (en) | 2012-04-19 | 2016-09-06 | Nant Holdings Ip, Llc | Mechanical Turk integrated IDE, systems and method |
US10147038B2 (en) | 2012-04-19 | 2018-12-04 | Nant Holdings Ip, Llc | Mechanical Turk integrated IDE, systems and methods |
US11113642B2 (en) | 2012-09-27 | 2021-09-07 | Commscope Connectivity Uk Limited | Mobile application for assisting a technician in carrying out an electronic work order |
US20140149513A1 (en) * | 2012-11-23 | 2014-05-29 | The Extraordinaries, Inc. | System and method for matching a profile to a sparsely defined request |
WO2014107517A1 (en) * | 2013-01-02 | 2014-07-10 | E-Rewards, Inc. | Priority-weighted quota cell selection to match a panelist to a market research project |
US10013481B2 (en) | 2013-01-02 | 2018-07-03 | Research Now Group, Inc. | Using a graph database to match entities by evaluating boolean expressions |
US9390195B2 (en) | 2013-01-02 | 2016-07-12 | Research Now Group, Inc. | Using a graph database to match entities by evaluating boolean expressions |
US9905089B2 (en) | 2013-09-04 | 2018-02-27 | Commscope Technologies Llc | Physical layer system with support for multiple active work orders and/or multiple active technicians |
US9407510B2 (en) | 2013-09-04 | 2016-08-02 | Commscope Technologies Llc | Physical layer system with support for multiple active work orders and/or multiple active technicians |
US9403482B2 (en) | 2013-11-22 | 2016-08-02 | At&T Intellectual Property I, L.P. | Enhanced view for connected cars |
US9866782B2 (en) | 2013-11-22 | 2018-01-09 | At&T Intellectual Property I, L.P. | Enhanced view for connected cars |
US10223699B2 (en) | 2014-08-05 | 2019-03-05 | CrowdCare Corporation | System and method of rule creation based on frequency of question |
US20160071048A1 (en) * | 2014-09-08 | 2016-03-10 | Xerox Corporation | Methods and systems for crowdsourcing of tasks |
US11210566B2 (en) * | 2018-09-27 | 2021-12-28 | Kabushiki Kaisha Toshiba | Training apparatus, training method and recording medium |
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